| #include "torch/csrc/python_headers.h" |
| #include <sys/types.h> |
| |
| #ifndef _MSC_VER |
| #include <sys/socket.h> |
| #endif |
| |
| #include <stdbool.h> |
| #include <unordered_map> |
| #include <libshm.h> |
| #include <TH/TH.h> |
| #include <ATen/ATen.h> |
| #include <ATen/ExpandUtils.h> |
| #include <ATen/dlpack.h> |
| #include <ATen/DLConvertor.h> |
| #include <pybind11/pybind11.h> |
| #include <pybind11/stl.h> |
| |
| #include "THP.h" |
| #include "torch/csrc/DynamicTypes.h" |
| #include "torch/csrc/Device.h" |
| #include "torch/csrc/Dtype.h" |
| #include "torch/csrc/DataLoader.h" |
| #include "torch/csrc/Generator.h" |
| #include "torch/csrc/Layout.h" |
| #include "torch/csrc/autograd/generated/python_nn_functions.h" |
| #include "torch/csrc/autograd/python_legacy_variable.h" |
| #include "torch/csrc/autograd/python_variable.h" |
| #include "torch/csrc/tensor/python_tensor.h" |
| #include "torch/csrc/utils/tensor_dtypes.h" |
| #include "torch/csrc/utils/python_strings.h" |
| #include "torch/csrc/utils/tensor_layouts.h" |
| #include "torch/csrc/utils/tensor_numpy.h" |
| #include "torch/csrc/jit/python_tracer.h" |
| #include "torch/csrc/jit/init.h" |
| #include "torch/csrc/jit/python_ir.h" |
| #include "torch/csrc/onnx/init.h" |
| |
| #ifdef WITH_CUDNN |
| #include "cudnn.h" |
| #endif |
| |
| #define WITH_NUMPY_IMPORT_ARRAY |
| #include "torch/csrc/utils/numpy_stub.h" |
| |
| namespace py = pybind11; |
| |
| PyObject* module; |
| |
| THPGenerator *THPDefaultGenerator = NULL; |
| |
| //////////////////////////////////////////////////////////////////////////////// |
| //////////////////////////////////////////////////////////////////////////////// |
| |
| static PyObject * THPModule_initNames(PyObject *self, PyObject *arg) |
| { |
| static std::vector<std::string> names; |
| |
| THPObjectPtr types(PySequence_Fast(arg, "expected a sequence")); |
| if (!types) return NULL; |
| |
| int num_classes = PySequence_Fast_GET_SIZE(types.get()); |
| names.reserve(names.size() + num_classes); |
| for (int i = 0; i < num_classes; i++) { |
| PyObject* obj = PySequence_Fast_GET_ITEM(types.get(), i); |
| THPUtils_assert(PyType_Check(obj), "expected a PyTypeObject"); |
| PyTypeObject* type = (PyTypeObject*)obj; |
| |
| THPObjectPtr module_name(PyObject_GetAttrString(obj, "__module__")); |
| if (!module_name) return NULL; |
| THPUtils_assert(THPUtils_checkString(module_name.get()), |
| "expected __module__ to be a string"); |
| std::string name = THPUtils_unpackString(module_name.get()); |
| names.push_back(name + "." + type->tp_name); |
| type->tp_name = names.back().c_str(); |
| } |
| Py_RETURN_NONE; |
| } |
| // |
| // Callback for python part. Used for additional initialization of python classes |
| static PyObject * THPModule_initExtension(PyObject *_unused, PyObject *shm_manager_path) |
| { |
| HANDLE_TH_ERRORS |
| if (!THPUtils_checkString(shm_manager_path)) { |
| THPUtils_setError("initialization error - expected bytes/string object as shm_manager_path!"); |
| return NULL; |
| } |
| torch::utils::initializeLayouts(); |
| torch::utils::initializeDtypes(); |
| torch::tensor::initialize_python_bindings(); |
| std::string path = THPUtils_unpackString(shm_manager_path); |
| libshm_init(path.c_str()); |
| |
| auto module = THPObjectPtr(PyImport_ImportModule("torch")); |
| if (!module) throw python_error(); |
| |
| THPDoubleStorage_postInit(module); |
| THPFloatStorage_postInit(module); |
| THPHalfStorage_postInit(module); |
| THPLongStorage_postInit(module); |
| THPIntStorage_postInit(module); |
| THPShortStorage_postInit(module); |
| THPCharStorage_postInit(module); |
| THPByteStorage_postInit(module); |
| THPAutograd_initFunctions(); |
| Py_RETURN_NONE; |
| END_HANDLE_TH_ERRORS |
| } |
| |
| static PyObject * THPModule_getNumThreads(PyObject *module) |
| { |
| return PyLong_FromLong(THGetNumThreads()); |
| } |
| |
| static PyObject * THPModule_setNumThreads(PyObject *module, PyObject *arg) |
| { |
| THPUtils_assert(THPUtils_checkLong(arg), "set_num_threads expects an int, " |
| "but got %s", THPUtils_typename(arg)); |
| THSetNumThreads((int)THPUtils_unpackLong(arg)); |
| at::set_num_threads((int)THPUtils_unpackLong(arg)); |
| Py_RETURN_NONE; |
| } |
| |
| PyObject * THPModule_setDefaultTensorType(PyObject *_unused, PyObject *type) |
| { |
| HANDLE_TH_ERRORS |
| torch::tensor::py_set_default_tensor_type(type); |
| Py_RETURN_NONE; |
| END_HANDLE_TH_ERRORS |
| } |
| |
| PyObject * THPModule_setDefaultDtype(PyObject *_unused, PyObject *dtype) |
| { |
| HANDLE_TH_ERRORS |
| torch::tensor::py_set_default_dtype(dtype); |
| Py_RETURN_NONE; |
| END_HANDLE_TH_ERRORS |
| } |
| |
| PyObject *THPModule_safeCall(PyObject *_unused, PyObject *args, PyObject *kwargs) |
| { |
| PyObject *result = NULL; |
| PyObject *args_slice = NULL; |
| PyThreadState *thread_state = PyThreadState_Get(); |
| Py_ssize_t num_args = args ? PyTuple_Size(args) : 0; |
| THPUtils_assert(num_args > 0, "expected at least one argument"); |
| try { |
| args_slice = PyTuple_GetSlice(args, 1, num_args); |
| result = PyObject_Call(PyTuple_GET_ITEM(args, 0), args_slice, kwargs); |
| } catch (std::exception &e) { |
| PyEval_RestoreThread(thread_state); |
| Py_DECREF(args_slice); |
| PyErr_SetString(THPException_FatalError, e.what()); |
| Py_LeaveRecursiveCall(); |
| } |
| Py_DECREF(args_slice); |
| return result; |
| } |
| |
| PyObject *THPModule_addDocStr(PyObject *_unused, PyObject *args) |
| { |
| // adds a __doc__ string to a function, similar to numpy's arr_add_docstring |
| static std::vector<std::string> all_docs; |
| PyObject *obj; |
| PyObject *doc_obj; |
| if (!PyArg_ParseTuple(args, "OO", &obj, &doc_obj)) { |
| return NULL; |
| } |
| |
| const char* doc_str = "<invalid string>"; |
| if (THPUtils_checkString(doc_obj)) { |
| all_docs.push_back(THPUtils_unpackString(doc_obj)); |
| doc_str = all_docs.back().c_str(); |
| } |
| |
| if (Py_TYPE(obj) == &PyCFunction_Type) { |
| PyCFunctionObject* f = (PyCFunctionObject *)obj; |
| if (f->m_ml->ml_doc) { |
| return PyErr_Format(PyExc_RuntimeError, |
| "function '%s' already has a docstring", f->m_ml->ml_name); |
| } |
| f->m_ml->ml_doc = doc_str; |
| } else if (strcmp(Py_TYPE(obj)->tp_name, "method_descriptor") == 0) { |
| PyMethodDescrObject* m = (PyMethodDescrObject *)obj; |
| if (m->d_method->ml_doc) { |
| return PyErr_Format(PyExc_RuntimeError, |
| "method '%s' already has a docstring", m->d_method->ml_name); |
| } |
| m->d_method->ml_doc = doc_str; |
| } else { |
| return PyErr_Format(PyExc_TypeError, |
| "don't know how to add docstring to type '%s'", Py_TYPE(obj)->tp_name); |
| } |
| |
| Py_INCREF(obj); |
| return obj; |
| } |
| |
| |
| PyObject *THPModule_inferSize(PyObject *_unused, PyObject *args) |
| { |
| HANDLE_TH_ERRORS |
| Py_ssize_t num_args = args ? (Py_ssize_t) PyTuple_Size(args) : 0; |
| THPUtils_assert(num_args == 2, "expected exactly 2 arguments"); |
| PyObject *arg1 = PyTuple_GET_ITEM(args, 0); |
| THPUtils_assert(THPSize_Check(arg1), "expected a torch.Size as argument 1"); |
| PyObject *arg2 = PyTuple_GET_ITEM(args, 1); |
| THPUtils_assert(THPSize_Check(arg2), "expected a torch.Size as argument 2"); |
| |
| auto size1 = THPUtils_unpackLongs(arg1); |
| auto size2 = THPUtils_unpackLongs(arg2); |
| auto sizes = at::infer_size(size1, size2); |
| return THPSize_New(sizes.size(), sizes.data()); |
| END_HANDLE_TH_ERRORS |
| } |
| |
| static PyObject *THPModule_setBackcompatBroadcastWarn(PyObject *module, PyObject *arg) { |
| THPUtils_assert(PyBool_Check(arg), "set_backcompat_broadcast_warn expects a bool, " |
| "but got %s", THPUtils_typename(arg)); |
| setBackCompatBroadcastWarn(arg == Py_True); |
| Py_RETURN_NONE; |
| } |
| |
| static PyObject *THPModule_getBackcompatBroadcastWarn(PyObject *module) |
| { |
| if (getBackCompatBroadcastWarn()) Py_RETURN_TRUE; |
| else Py_RETURN_FALSE; |
| } |
| |
| static PyObject *THPModule_setBackcompatKeepdimWarn(PyObject *module, PyObject *arg) { |
| THPUtils_assert(PyBool_Check(arg), "set_backcompat_keepdim_warn expects a bool, " |
| "but got %s", THPUtils_typename(arg)); |
| setBackCompatKeepdimWarn(arg == Py_True); |
| Py_RETURN_NONE; |
| } |
| |
| static PyObject *THPModule_getBackcompatKeepdimWarn(PyObject *module) |
| { |
| if (getBackCompatKeepdimWarn()) Py_RETURN_TRUE; |
| else Py_RETURN_FALSE; |
| } |
| |
| PyObject *THPModule_hasDistributed(PyObject *_unused) |
| { |
| #ifdef WITH_DISTRIBUTED |
| Py_RETURN_TRUE; |
| #else |
| Py_RETURN_FALSE; |
| #endif |
| } |
| |
| PyObject *THPModule_toDLPack(PyObject *_unused, PyObject *data) |
| { |
| HANDLE_TH_ERRORS |
| THPUtils_assert(THPVariable_Check(data), "data must be a Tensor"); |
| DLManagedTensor* dlMTensor = at::toDLPack(THPVariable_UnpackData(data)); |
| return PyCapsule_New(dlMTensor, "dltensor", NULL); |
| END_HANDLE_TH_ERRORS |
| } |
| |
| PyObject *THPModule_fromDLPack(PyObject *_unused, PyObject *data) |
| { |
| using namespace torch::autograd; |
| HANDLE_TH_ERRORS |
| DLManagedTensor * dlMTensor = (DLManagedTensor *)PyCapsule_GetPointer(data, "dltensor"); |
| THPUtils_assert(dlMTensor, "from_dlpack received an invalid capsule. " |
| "Note that DLTensor capsules can be consumed only once, " |
| "so you might have already constructed a tensor from it once.") |
| // atensor steals the ownership of the underlying storage. It also passes a |
| // destructor function that will be called when the underlying storage goes |
| // out of scope. When the destructor is called, the dlMTensor is destructed too. |
| auto atensor = make_variable(at::fromDLPack(dlMTensor), false); |
| |
| // It is possible that the call to at::fromDLPack is the very first |
| // call to create a Tensor in PyTorch. If so, then _lazy_init has |
| // not been called, and the attempt to call createPyObject will fail |
| // because cuda ATen types have not been registered in Python yet. |
| // so if we have a cuda tensor, then we need to make sure |
| // we have called _lazy_init here |
| if(atensor.is_cuda()) { |
| py::module::import("torch.cuda").attr("init")(); |
| } |
| // Make sure this capsule will never be used again. |
| PyCapsule_SetName(data, "used_dltensor"); |
| return THPVariable_Wrap(std::move(atensor)); |
| END_HANDLE_TH_ERRORS |
| } |
| |
| PyObject *THPModule_setUserEnabledCuDNN(PyObject *_unused, PyObject *arg) |
| { |
| THPUtils_assert(PyBool_Check(arg), "set_enabled_cudnn expects a bool, " |
| "but got %s", THPUtils_typename(arg)); |
| at::globalContext().setUserEnabledCuDNN(arg == Py_True); |
| Py_RETURN_NONE; |
| } |
| |
| PyObject *THPModule_userEnabledCuDNN(PyObject *_unused) |
| { |
| if (at::globalContext().userEnabledCuDNN()) Py_RETURN_TRUE; |
| else Py_RETURN_FALSE; |
| } |
| |
| PyObject *THPModule_setDeterministicCuDNN(PyObject *_unused, PyObject *arg) |
| { |
| THPUtils_assert(PyBool_Check(arg), "set_deterministic_cudnn expects a bool, " |
| "but got %s", THPUtils_typename(arg)); |
| at::globalContext().setDeterministicCuDNN(arg == Py_True); |
| Py_RETURN_NONE; |
| } |
| |
| PyObject *THPModule_deterministicCuDNN(PyObject *_unused) |
| { |
| if (at::globalContext().deterministicCuDNN()) Py_RETURN_TRUE; |
| else Py_RETURN_FALSE; |
| } |
| |
| PyObject *THPModule_setBenchmarkCuDNN(PyObject *_unused, PyObject *arg) |
| { |
| THPUtils_assert(PyBool_Check(arg), "set_benchmark_cudnn expects a bool, " |
| "but got %s", THPUtils_typename(arg)); |
| at::globalContext().setBenchmarkCuDNN(arg == Py_True); |
| Py_RETURN_NONE; |
| } |
| |
| PyObject *THPModule_benchmarkCuDNN(PyObject *_unused) |
| { |
| if (at::globalContext().benchmarkCuDNN()) Py_RETURN_TRUE; |
| else Py_RETURN_FALSE; |
| } |
| |
| PyObject *THPModule_setFlushDenormal(PyObject *_unused, PyObject *arg) { |
| THPUtils_assert(PyBool_Check(arg), "flush_denormal expects a bool, " |
| "but got %s", THPUtils_typename(arg)); |
| if (!at::globalContext().setFlushDenormal(arg == Py_True)) { |
| Py_RETURN_FALSE; |
| }; |
| Py_RETURN_TRUE; |
| } |
| |
| PyObject *THPModule_getDefaultDtype(PyObject *_unused, PyObject *arg) { |
| HANDLE_TH_ERRORS |
| auto& type = torch::tensor::get_default_tensor_type(); |
| auto dtype = (PyObject*)torch::getDtype(type.scalarType()); |
| Py_INCREF(dtype); |
| return dtype; |
| END_HANDLE_TH_ERRORS |
| } |
| |
| PyObject *THPModule_isDefaultTypeCuda(PyObject *_unused, PyObject *arg) { |
| HANDLE_TH_ERRORS |
| if (torch::tensor::get_default_tensor_type().is_cuda()) { |
| Py_RETURN_TRUE; |
| } |
| Py_RETURN_FALSE; |
| END_HANDLE_TH_ERRORS |
| } |
| |
| static PyMethodDef TorchMethods[] = { |
| {"_initExtension", (PyCFunction)THPModule_initExtension, METH_O, NULL}, |
| {"_autograd_init", (PyCFunction)THPAutograd_initExtension, METH_NOARGS, NULL}, |
| {"_add_docstr", (PyCFunction)THPModule_addDocStr, METH_VARARGS, NULL}, |
| {"_init_names", (PyCFunction)THPModule_initNames, METH_O, NULL}, |
| {"_has_distributed",(PyCFunction)THPModule_hasDistributed, METH_NOARGS, NULL}, |
| {"_safe_call", (PyCFunction)THPModule_safeCall, METH_VARARGS | METH_KEYWORDS, NULL}, |
| {"_set_default_tensor_type", (PyCFunction)THPModule_setDefaultTensorType, METH_O, NULL}, |
| {"_set_default_dtype", (PyCFunction)THPModule_setDefaultDtype, METH_O, NULL}, |
| {"_infer_size", (PyCFunction)THPModule_inferSize, METH_VARARGS, NULL}, |
| {"_set_backcompat_broadcast_warn", (PyCFunction)THPModule_setBackcompatBroadcastWarn, METH_O, NULL}, |
| {"_get_backcompat_broadcast_warn", (PyCFunction)THPModule_getBackcompatBroadcastWarn, METH_NOARGS, NULL}, |
| {"_set_backcompat_keepdim_warn", (PyCFunction)THPModule_setBackcompatKeepdimWarn, METH_O, NULL}, |
| {"_get_backcompat_keepdim_warn", (PyCFunction)THPModule_getBackcompatKeepdimWarn, METH_NOARGS, NULL}, |
| {"get_num_threads", (PyCFunction)THPModule_getNumThreads, METH_NOARGS, NULL}, |
| {"set_num_threads", (PyCFunction)THPModule_setNumThreads, METH_O, NULL}, |
| {"_get_cudnn_enabled", (PyCFunction)THPModule_userEnabledCuDNN, METH_NOARGS, NULL}, |
| {"_set_cudnn_enabled", (PyCFunction)THPModule_setUserEnabledCuDNN, METH_O, NULL}, |
| {"_get_cudnn_benchmark", (PyCFunction)THPModule_benchmarkCuDNN, METH_NOARGS, NULL}, |
| {"_set_cudnn_benchmark", (PyCFunction)THPModule_setBenchmarkCuDNN, METH_O, NULL}, |
| {"_get_cudnn_deterministic", (PyCFunction)THPModule_deterministicCuDNN, METH_NOARGS, NULL}, |
| {"_set_cudnn_deterministic", (PyCFunction)THPModule_setDeterministicCuDNN, METH_O, NULL}, |
| {"_to_dlpack", (PyCFunction)THPModule_toDLPack, METH_O, NULL}, |
| {"_from_dlpack", (PyCFunction)THPModule_fromDLPack, METH_O, NULL}, |
| {"set_flush_denormal", (PyCFunction)THPModule_setFlushDenormal, METH_O, NULL}, |
| {"get_default_dtype", (PyCFunction)THPModule_getDefaultDtype, METH_NOARGS, NULL}, |
| {"_is_default_type_cuda", (PyCFunction)THPModule_isDefaultTypeCuda, METH_NOARGS, NULL}, |
| {NULL, NULL, 0, NULL} |
| }; |
| |
| bool THCPDoubleStorage_init(PyObject *module); |
| bool THCPFloatStorage_init(PyObject *module); |
| bool THCPHalfStorage_init(PyObject *module); |
| bool THCPLongStorage_init(PyObject *module); |
| bool THCPIntStorage_init(PyObject *module); |
| bool THCPShortStorage_init(PyObject *module); |
| bool THCPCharStorage_init(PyObject *module); |
| bool THCPByteStorage_init(PyObject *module); |
| |
| bool THCPStream_init(PyObject *module); |
| |
| #ifdef WITH_CUDA |
| PyMethodDef* THCPModule_methods(); |
| namespace torch { namespace cuda { |
| |
| void initModule(PyObject *module); |
| |
| }} // namespace torch::cuda |
| #endif |
| |
| namespace torch { namespace nn { |
| |
| void init__THNN(PyObject*); |
| #ifdef WITH_CUDA |
| void init__THCUNN(PyObject*); |
| #endif |
| |
| }} // namespace torch::nn |
| |
| bool THDPDoubleStorage_init(PyObject *module); |
| bool THDPFloatStorage_init(PyObject *module); |
| //bool THDPHalfStorage_init(PyObject *module); |
| bool THDPLongStorage_init(PyObject *module); |
| bool THDPIntStorage_init(PyObject *module); |
| bool THDPShortStorage_init(PyObject *module); |
| bool THDPCharStorage_init(PyObject *module); |
| bool THDPByteStorage_init(PyObject *module); |
| |
| static std::vector<PyMethodDef> methods; |
| |
| #ifdef WITH_DISTRIBUTED |
| PyMethodDef* THDPModule_methods(); |
| #endif |
| |
| // TODO: Refactor this in some less manual way |
| #ifdef WITH_CUDNN |
| static PyObject * THCUDNN_cudnn_version(PyObject *self, PyObject *args) |
| { |
| return PyLong_FromLong(CUDNN_VERSION); |
| } |
| |
| static PyMethodDef _THCUDNN_methods[] = { |
| {"_cudnn_version", (PyCFunction)THCUDNN_cudnn_version, METH_VARARGS, NULL}, |
| {NULL} |
| }; |
| |
| PyMethodDef* THCUDNN_methods() { |
| return _THCUDNN_methods; |
| } |
| #endif |
| |
| static PyObject* initModule() { |
| HANDLE_TH_ERRORS |
| THInferNumThreads(); |
| |
| #define ASSERT_TRUE(cmd) if (!(cmd)) return NULL |
| |
| THPUtils_addPyMethodDefs(methods, TorchMethods); |
| THPUtils_addPyMethodDefs(methods, DataLoaderMethods); |
| THPUtils_addPyMethodDefs(methods, torch::autograd::python_functions()); |
| #ifdef WITH_CUDA |
| THPUtils_addPyMethodDefs(methods, THCPModule_methods()); |
| #endif |
| #ifdef WITH_CUDNN |
| THPUtils_addPyMethodDefs(methods, THCUDNN_methods()); |
| #endif |
| #ifdef WITH_DISTRIBUTED |
| THPUtils_addPyMethodDefs(methods, THDPModule_methods()); |
| #endif |
| |
| #if PY_MAJOR_VERSION == 2 |
| ASSERT_TRUE(module = Py_InitModule("torch._C", methods.data())); |
| #else |
| static struct PyModuleDef torchmodule = { |
| PyModuleDef_HEAD_INIT, |
| "torch._C", |
| NULL, |
| -1, |
| methods.data() |
| }; |
| ASSERT_TRUE(module = PyModule_Create(&torchmodule)); |
| #endif |
| ASSERT_TRUE(THPWrapper_init(module)); |
| ASSERT_TRUE(THPGenerator_init(module)); |
| ASSERT_TRUE(THPException_init(module)); |
| THPSize_init(module); |
| THPDtype_init(module); |
| THPLayout_init(module); |
| THPDevice_init(module); |
| ASSERT_TRUE(THPVariable_initModule(module)); |
| ASSERT_TRUE(THPFunction_initModule(module)); |
| ASSERT_TRUE(THPEngine_initModule(module)); |
| torch::autograd::initAutogradClosureBindings(module); |
| torch::jit::initJITBindings(module); |
| torch::onnx::initONNXBindings(module); |
| torch::autograd::initNNFunctions(module); |
| torch::autograd::init_legacy_variable(module); |
| #ifdef WITH_CUDA |
| torch::cuda::initModule(module); |
| #endif |
| ASSERT_TRUE(THPDoubleStorage_init(module)); |
| ASSERT_TRUE(THPFloatStorage_init(module)); |
| ASSERT_TRUE(THPHalfStorage_init(module)); |
| ASSERT_TRUE(THPLongStorage_init(module)); |
| ASSERT_TRUE(THPIntStorage_init(module)); |
| ASSERT_TRUE(THPShortStorage_init(module)); |
| ASSERT_TRUE(THPCharStorage_init(module)); |
| ASSERT_TRUE(THPByteStorage_init(module)); |
| |
| #ifdef WITH_CUDA |
| // This will only initialise base classes and attach them to library namespace |
| // They won't be ready for real usage until importing cuda module, that will |
| // complete the process (but it defines Python classes before calling back into |
| // C, so these lines have to execute first).. |
| ASSERT_TRUE(THCPDoubleStorage_init(module)); |
| ASSERT_TRUE(THCPFloatStorage_init(module)); |
| ASSERT_TRUE(THCPHalfStorage_init(module)); |
| ASSERT_TRUE(THCPLongStorage_init(module)); |
| ASSERT_TRUE(THCPIntStorage_init(module)); |
| ASSERT_TRUE(THCPShortStorage_init(module)); |
| ASSERT_TRUE(THCPCharStorage_init(module)); |
| ASSERT_TRUE(THCPByteStorage_init(module)); |
| |
| ASSERT_TRUE(THCPStream_init(module)); |
| #endif |
| |
| #ifdef WITH_CUDNN |
| PyObject *has_cudnn = Py_True; |
| #else |
| PyObject *has_cudnn = Py_False; |
| #endif |
| Py_INCREF(has_cudnn); |
| ASSERT_TRUE(PyModule_AddObject(module, "has_cudnn", has_cudnn) == 0); |
| |
| #ifdef WITH_DISTRIBUTED_MW |
| // See comment on CUDA objects |
| ASSERT_TRUE(THDPDoubleStorage_init(module)); |
| ASSERT_TRUE(THDPFloatStorage_init(module)); |
| //ASSERT_TRUE(THDPHalfStorage_init(module)); |
| ASSERT_TRUE(THDPLongStorage_init(module)); |
| ASSERT_TRUE(THDPIntStorage_init(module)); |
| ASSERT_TRUE(THDPShortStorage_init(module)); |
| ASSERT_TRUE(THDPCharStorage_init(module)); |
| ASSERT_TRUE(THDPByteStorage_init(module)); |
| #endif |
| |
| // force ATen to initialize because it handles |
| // setting up TH Errors so that they throw C++ exceptions |
| at::init(); |
| |
| ASSERT_TRUE(PyModule_AddObject(module, "has_mkl", at::hasMKL() ? Py_True : Py_False) == 0); |
| |
| auto& defaultGenerator = at::globalContext().defaultGenerator(at::kCPU); |
| THPDefaultGenerator = (THPGenerator*)THPGenerator_NewWithGenerator( |
| defaultGenerator); |
| ASSERT_TRUE(PyModule_AddObject(module, "default_generator", (PyObject*)THPDefaultGenerator) == 0); |
| |
| #ifdef WITH_NUMPY |
| if (_import_array() < 0) return NULL; |
| #endif |
| |
| torch::nn::init__THNN(module); |
| #ifdef WITH_CUDA |
| torch::nn::init__THCUNN(module); |
| #endif |
| |
| return module; |
| END_HANDLE_TH_ERRORS |
| } |
| |
| // Checks that the _C shared library isn't initialized multiple times. This |
| // can happen if the same csrc files are compiled into multiple shared |
| // libraries. |
| inline void pytorch_duplicate_guard() { |
| static int initialized = 0; |
| if (initialized) { |
| fprintf(stderr, "pytorch: _C shared library re-initialized\n"); |
| abort(); |
| } |
| initialized = 1; |
| ;} |
| |
| struct call_duplicate_guard { |
| call_duplicate_guard() { pytorch_duplicate_guard(); } |
| }; |
| |
| static call_duplicate_guard _call_duplicate_guard; |
| |
| #if PY_MAJOR_VERSION == 2 |
| PyMODINIT_FUNC init_C() |
| #else |
| PyMODINIT_FUNC PyInit__C() |
| #endif |
| { |
| #if PY_MAJOR_VERSION == 2 |
| initModule(); |
| #else |
| return initModule(); |
| #endif |
| } |